{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/rlworkgroup/garage/blob/master/examples/jupyter/trpo_gym_tf_cartpole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "acXH8kwHsAYT"
   },
   "source": [
    "This is a jupyter notebook demonstrating usage of [garage](https://github.com/rlworkgroup/garage) in a jupyter notebook.\n",
    "\n",
    "In particular, it demonstrates the example `trpo_gym_tf_cartpole.py` file already available in [garage/examples/tf/](https://github.com/rlworkgroup/garage/blob/master/examples/tf/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GmPLYXrXH7A5"
   },
   "source": [
    "## Install pre-requisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1655
    },
    "colab_type": "code",
    "id": "Aj3bL9HaG5HL",
    "outputId": "6d152547-07e8-4f32-9cf0-882fc97620ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'garage' already exists and is not an empty directory.\n",
      "/bin/sh: /dev/null!: Operation not permitted\n"
     ]
    }
   ],
   "source": [
    "!echo \"abcd\" > mujoco_fake_key\n",
    "\n",
    "\n",
    "!git clone --depth 1 https://github.com/rlworkgroup/garage/\n",
    "\n",
    "!cd garage\n",
    "!bash scripts/setup_colab.sh --mjkey ../mujoco_fake_key --no-modify-bashrc > /dev/null!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 169
    },
    "colab_type": "code",
    "id": "6dGO3j0txY_l",
    "outputId": "43ea0b65-bb1b-4977-acd0-53c8d761dd27"
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Please restart your runtime so that the installed dependencies for 'garage' can be loaded, and then resume running the notebook",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-0a7126b9b224>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please restart your runtime so that the installed dependencies for 'garage' can be loaded, and then resume running the notebook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mException\u001b[0m: Please restart your runtime so that the installed dependencies for 'garage' can be loaded, and then resume running the notebook"
     ]
    }
   ],
   "source": [
    "raise Exception(\"Please restart your runtime so that the installed dependencies for 'garage' can be loaded, and then resume running the notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pGyQDPC2Vs9O"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G3KbL5sQH9m1"
   },
   "source": [
    "## Import dependencies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IPmbM_77tk3S"
   },
   "outputs": [],
   "source": [
    "# The contents of this cell and the one after are mostly copied from garage/examples/...\n",
    "# Note that these need to be run twice if for the first time on a colab.research.google.com instance\n",
    "# 1st time is to create the \"personal config from template\"\n",
    "# 2nd time is the charm\n",
    "import os\n",
    "import garage\n",
    "from garage.np.baselines import LinearFeatureBaseline\n",
    "from garage.envs import GymEnv\n",
    "from garage.envs import normalize\n",
    "from garage.tf.algos import TRPO\n",
    "#from garage.tf.policies import GaussianMLPPolicy\n",
    "from garage.tf.policies import CategoricalMLPPolicy\n",
    "\n",
    "import gym\n",
    "\n",
    "from garage.experiment import TFTrainer\n",
    "from garage.experiment.deterministic import set_seed\n",
    "from dowel import logger, StdOutput\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the dowel logger\n",
    "log_dir = os.path.join(os.getcwd(), 'data')\n",
    "ctxt=garage.experiment.SnapshotConfig(snapshot_dir=log_dir,\n",
    "                                      snapshot_mode='last',\n",
    "                                      snapshot_gap=1)\n",
    "\n",
    "# log to stdout\n",
    "logger.add_output(StdOutput())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up video visualization of the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 795
    },
    "colab_type": "code",
    "id": "OQHSMjkYQoPq",
    "outputId": "68193720-6fb2-4998-bdba-dae9019a3277"
   },
   "outputs": [],
   "source": [
    "# tested and worked for linux os\n",
    "# Prepare display for seeing a video of the policy in action in the jupyter notebook\n",
    "# Note that this doesn't require a runtime restart\n",
    "# https://stackoverflow.com/a/51183488/4126114\n",
    "\n",
    "# Linux installation\n",
    "!apt-get install python-opengl ffmpeg xvfb\n",
    "!pip3 install pyvirtualdisplay "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 62
    },
    "colab_type": "code",
    "id": "i4kwHJFkGB9R",
    "outputId": "ffaf04a7-fb30-4daa-9776-b9501cce7250"
   },
   "outputs": [],
   "source": [
    "# Virtual display (for Linux only)\n",
    "from pyvirtualdisplay import Display\n",
    "\n",
    "# launch stuff inside virtual display here\n",
    "\n",
    "virtual_display = Display(visible=0, size=(1400, 900))\n",
    "virtual_display.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyvirtualdisplay in /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages (1.3.2)\n",
      "Requirement already satisfied: EasyProcess in /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages (from pyvirtualdisplay) (0.3)\n",
      "\u001b[33mYou are using pip version 18.1, however version 20.2b1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: PyOpenGL in /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages (3.1.5)\n",
      "Requirement already satisfied: ffmpeg in /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages (1.4)\n",
      "\u001b[33mYou are using pip version 18.1, however version 20.2b1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# MacOS installation\n",
    "!pip3 install pyvirtualdisplay \n",
    "! pip install PyOpenGL ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_algorithm_as_video(env, policy):\n",
    "            env.spec.id = 1\n",
    "            env = wrappers.Monitor(env, \"./gym-results\", force=True)\n",
    "            obs = env.reset()\n",
    "            for i in range(1000):\n",
    "                #action = env.action_space.sample()\n",
    "                action, _ = policy.get_action(obs)\n",
    "                obs, reward, done, info = env.step(action)\n",
    "                if done: break\n",
    "\n",
    "            print(\"done at step %i\"%i)\n",
    "            env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rjkcy1nUIFYX"
   },
   "source": [
    "## Define and train the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "from gym import wrappers\n",
    "def trpo_gym_tf_cartpole(ctxt=None, seed=1):\n",
    "    \"\"\"Train TRPO with CartPole-v0 environment.\n",
    "    \n",
    "    Args:\n",
    "        ctxt (garage.experiment.ExperimentContext): The experiment\n",
    "            configuration used by Trainer to create the snapshotter.\n",
    "        seed (int): Used to seed the random number generator to produce\n",
    "            determinism.\n",
    "\n",
    "    \"\"\"\n",
    "    set_seed(seed)\n",
    "    with TFTrainer(snapshot_config=ctxt) as trainer:\n",
    "        # garage version of CartPole environment, has Discrete action space instead of Box\n",
    "        ###########################################\n",
    "        # env = normalize(GymEnv(CartpoleEnv()))\n",
    "        # policy = GaussianMLPPolicy(\n",
    "        #    name=\"policy\", env_spec=env.spec, hidden_sizes=(32, 32))\n",
    "        \n",
    "        # gym version of CartPole. Check note above\n",
    "        ###########################################\n",
    "        # env = normalize(GymEnv(gym.make(\"CartPole-v0\")))\n",
    "        \n",
    "        # garage's updated method of getting env\n",
    "        env = GymEnv(gym.make('CartPole-v1'))\n",
    "\n",
    "        policy = CategoricalMLPPolicy(name='policy',\n",
    "                                      env_spec=env.spec,\n",
    "                                      hidden_sizes=(32, 32))\n",
    "\n",
    "         # create baseline\n",
    "        baseline = LinearFeatureBaseline(env_spec=env.spec)\n",
    "\n",
    "        # specify an algorithm\n",
    "        algo = TRPO(\n",
    "            env_spec=env.spec,\n",
    "            policy=policy,\n",
    "            baseline=baseline,\n",
    "            max_episode_length=200,\n",
    "            discount=0.99,\n",
    "            max_kl_step=0.01,\n",
    "        )\n",
    "\n",
    "        # train the algorithm\n",
    "        trainer.setup(algo, env)\n",
    "        trainer.train(n_epochs=10, batch_size=10000, plot=False)\n",
    "        \n",
    "        visualize_algorithm_as_video(env, policy)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-06-17 23:39:28 | Setting seed to 1\n",
      "WARNING:tensorflow:From /Users/irisliu/src/resl/garage/src/garage/tf/models/mlp.py:84: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages/tensorflow/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From /Users/irisliu/src/resl/garage/src/garage/tf/models/model.py:345: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Prefer Variable.assign which has equivalent behavior in 2.X.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/irisliu/src/resl/garage/src/garage/experiment/local_tf_trainer.py:182: LoggerWarning: \u001b[33mLog data of type Graph was not accepted by any output\u001b[0m\n",
      "  logger.log(self.sess.graph)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-06-17 23:39:28 | Obtaining samples...\n",
      "2020-06-17 23:39:28 | epoch #0 | Obtaining samples for iteration 0...\n",
      "2020-06-17 23:39:30 | epoch #0 | Logging diagnostics...\n",
      "2020-06-17 23:39:30 | epoch #0 | Optimizing policy...\n",
      "2020-06-17 23:39:30 | epoch #0 | Computing loss before\n",
      "2020-06-17 23:39:30 | epoch #0 | Computing KL before\n",
      "2020-06-17 23:39:30 | epoch #0 | Optimizing\n",
      "2020-06-17 23:39:30 | epoch #0 | Start CG optimization: #parameters: 1282, #inputs: 286, #subsample_inputs: 286\n",
      "2020-06-17 23:39:30 | epoch #0 | computing loss before\n",
      "2020-06-17 23:39:30 | epoch #0 | computing gradient\n",
      "2020-06-17 23:39:30 | epoch #0 | gradient computed\n",
      "2020-06-17 23:39:30 | epoch #0 | computing descent direction\n",
      "2020-06-17 23:39:32 | epoch #0 | descent direction computed\n",
      "2020-06-17 23:39:32 | epoch #0 | backtrack iters: 8\n",
      "2020-06-17 23:39:32 | epoch #0 | optimization finished\n",
      "2020-06-17 23:39:32 | epoch #0 | Computing KL after\n",
      "2020-06-17 23:39:32 | epoch #0 | Computing loss after\n",
      "2020-06-17 23:39:32 | epoch #0 | Fitting baseline...\n",
      "2020-06-17 23:39:32 | epoch #0 | Saving snapshot...\n",
      "2020-06-17 23:39:32 | epoch #0 | Saved\n",
      "2020-06-17 23:39:32 | epoch #0 | Time 3.84 s\n",
      "2020-06-17 23:39:32 | epoch #0 | EpochTime 3.84 s\n",
      "---------------------------------------  ---------------\n",
      "EnvExecTime                                  0.258774\n",
      "Evaluation/AverageDiscountedReturn          28.4098\n",
      "Evaluation/AverageReturn                    35.1014\n",
      "Evaluation/TerminationRate                   1\n",
      "Evaluation/Iteration                         0\n",
      "Evaluation/MaxReturn                       121\n",
      "Evaluation/MinReturn                         9\n",
      "Evaluation/NumEpisodes                     286\n",
      "Evaluation/StdReturn                        20.1039\n",
      "Extras/EpisodeRewardMean                    35.22\n",
      "LinearFeatureBaseline/ExplainedVariance     -1.44132e-08\n",
      "PolicyExecTime                               1.12909\n",
      "ProcessExecTime                              0.0701289\n",
      "TotalEnvSteps                            10039\n",
      "policy/Entropy                               3.89006\n",
      "policy/KL                                    0.00466172\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.684885\n",
      "policy/LossBefore                           -0.666195\n",
      "policy/Perplexity                           48.9136\n",
      "policy/dLoss                                 0.0186895\n",
      "---------------------------------------  ---------------\n",
      "2020-06-17 23:39:32 | epoch #1 | Obtaining samples for iteration 1...\n",
      "2020-06-17 23:39:34 | epoch #1 | Logging diagnostics...\n",
      "2020-06-17 23:39:34 | epoch #1 | Optimizing policy...\n",
      "2020-06-17 23:39:34 | epoch #1 | Computing loss before\n",
      "2020-06-17 23:39:34 | epoch #1 | Computing KL before\n",
      "2020-06-17 23:39:34 | epoch #1 | Optimizing\n",
      "2020-06-17 23:39:34 | epoch #1 | Start CG optimization: #parameters: 1282, #inputs: 248, #subsample_inputs: 248\n",
      "2020-06-17 23:39:34 | epoch #1 | computing loss before\n",
      "2020-06-17 23:39:34 | epoch #1 | computing gradient\n",
      "2020-06-17 23:39:34 | epoch #1 | gradient computed\n",
      "2020-06-17 23:39:34 | epoch #1 | computing descent direction\n",
      "2020-06-17 23:39:35 | epoch #1 | descent direction computed\n",
      "2020-06-17 23:39:35 | epoch #1 | backtrack iters: 3\n",
      "2020-06-17 23:39:35 | epoch #1 | optimization finished\n",
      "2020-06-17 23:39:35 | epoch #1 | Computing KL after\n",
      "2020-06-17 23:39:35 | epoch #1 | Computing loss after\n",
      "2020-06-17 23:39:35 | epoch #1 | Fitting baseline...\n",
      "2020-06-17 23:39:35 | epoch #1 | Saving snapshot...\n",
      "2020-06-17 23:39:35 | epoch #1 | Saved\n",
      "2020-06-17 23:39:35 | epoch #1 | Time 6.52 s\n",
      "2020-06-17 23:39:35 | epoch #1 | EpochTime 2.67 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.269802\n",
      "Evaluation/AverageDiscountedReturn          31.2977\n",
      "Evaluation/AverageReturn                    40.6331\n",
      "Evaluation/TerminationRate                   0.995968\n",
      "Evaluation/Iteration                         1\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                        10\n",
      "Evaluation/NumEpisodes                     248\n",
      "Evaluation/StdReturn                        27.9663\n",
      "Extras/EpisodeRewardMean                    38.83\n",
      "LinearFeatureBaseline/ExplainedVariance      0.228562\n",
      "PolicyExecTime                               1.12142\n",
      "ProcessExecTime                              0.0771155\n",
      "TotalEnvSteps                            20116\n",
      "policy/Entropy                               3.34935\n",
      "policy/KL                                    0.00585307\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.382747\n",
      "policy/LossBefore                           -0.318565\n",
      "policy/Perplexity                           28.4843\n",
      "policy/dLoss                                 0.0641819\n",
      "---------------------------------------  --------------\n",
      "2020-06-17 23:39:35 | epoch #2 | Obtaining samples for iteration 2...\n",
      "2020-06-17 23:39:36 | epoch #2 | Logging diagnostics...\n",
      "2020-06-17 23:39:36 | epoch #2 | Optimizing policy...\n",
      "2020-06-17 23:39:36 | epoch #2 | Computing loss before\n",
      "2020-06-17 23:39:36 | epoch #2 | Computing KL before\n",
      "2020-06-17 23:39:36 | epoch #2 | Optimizing\n",
      "2020-06-17 23:39:36 | epoch #2 | Start CG optimization: #parameters: 1282, #inputs: 138, #subsample_inputs: 138\n",
      "2020-06-17 23:39:36 | epoch #2 | computing loss before\n",
      "2020-06-17 23:39:36 | epoch #2 | computing gradient\n",
      "2020-06-17 23:39:36 | epoch #2 | gradient computed\n",
      "2020-06-17 23:39:36 | epoch #2 | computing descent direction\n",
      "2020-06-17 23:39:37 | epoch #2 | descent direction computed\n",
      "2020-06-17 23:39:37 | epoch #2 | backtrack iters: 1\n",
      "2020-06-17 23:39:37 | epoch #2 | optimization finished\n",
      "2020-06-17 23:39:37 | epoch #2 | Computing KL after\n",
      "2020-06-17 23:39:37 | epoch #2 | Computing loss after\n",
      "2020-06-17 23:39:37 | epoch #2 | Fitting baseline...\n",
      "2020-06-17 23:39:37 | epoch #2 | Saving snapshot...\n",
      "2020-06-17 23:39:37 | epoch #2 | Saved\n",
      "2020-06-17 23:39:37 | epoch #2 | Time 8.58 s\n",
      "2020-06-17 23:39:37 | epoch #2 | EpochTime 2.06 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.249501\n",
      "Evaluation/AverageDiscountedReturn          46.1977\n",
      "Evaluation/AverageReturn                    72.6377\n",
      "Evaluation/TerminationRate                   0.956522\n",
      "Evaluation/Iteration                         2\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                         8\n",
      "Evaluation/NumEpisodes                     138\n",
      "Evaluation/StdReturn                        50.6508\n",
      "Extras/EpisodeRewardMean                    74.88\n",
      "LinearFeatureBaseline/ExplainedVariance      0.0968584\n",
      "PolicyExecTime                               1.04033\n",
      "ProcessExecTime                              0.0689116\n",
      "TotalEnvSteps                            30140\n",
      "policy/Entropy                               1.81673\n",
      "policy/KL                                    0.00728534\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.304921\n",
      "policy/LossBefore                           -0.271817\n",
      "policy/Perplexity                            6.15173\n",
      "policy/dLoss                                 0.033104\n",
      "---------------------------------------  --------------\n",
      "2020-06-17 23:39:37 | epoch #3 | Obtaining samples for iteration 3...\n",
      "2020-06-17 23:39:38 | epoch #3 | Logging diagnostics...\n",
      "2020-06-17 23:39:38 | epoch #3 | Optimizing policy...\n",
      "2020-06-17 23:39:38 | epoch #3 | Computing loss before\n",
      "2020-06-17 23:39:38 | epoch #3 | Computing KL before\n",
      "2020-06-17 23:39:38 | epoch #3 | Optimizing\n",
      "2020-06-17 23:39:38 | epoch #3 | Start CG optimization: #parameters: 1282, #inputs: 94, #subsample_inputs: 94\n",
      "2020-06-17 23:39:38 | epoch #3 | computing loss before\n",
      "2020-06-17 23:39:38 | epoch #3 | computing gradient\n",
      "2020-06-17 23:39:38 | epoch #3 | gradient computed\n",
      "2020-06-17 23:39:38 | epoch #3 | computing descent direction\n",
      "2020-06-17 23:39:39 | epoch #3 | descent direction computed\n",
      "2020-06-17 23:39:39 | epoch #3 | backtrack iters: 0\n",
      "2020-06-17 23:39:39 | epoch #3 | optimization finished\n",
      "2020-06-17 23:39:39 | epoch #3 | Computing KL after\n",
      "2020-06-17 23:39:39 | epoch #3 | Computing loss after\n",
      "2020-06-17 23:39:39 | epoch #3 | Fitting baseline...\n",
      "2020-06-17 23:39:39 | epoch #3 | Saving snapshot...\n",
      "2020-06-17 23:39:39 | epoch #3 | Saved\n",
      "2020-06-17 23:39:39 | epoch #3 | Time 10.47 s\n",
      "2020-06-17 23:39:39 | epoch #3 | EpochTime 1.89 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.256417\n",
      "Evaluation/AverageDiscountedReturn          59.7693\n",
      "Evaluation/AverageReturn                   107.83\n",
      "Evaluation/TerminationRate                   0.882979\n",
      "Evaluation/Iteration                         3\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                        11\n",
      "Evaluation/NumEpisodes                      94\n",
      "Evaluation/StdReturn                        60.1331\n",
      "Extras/EpisodeRewardMean                   105.98\n",
      "LinearFeatureBaseline/ExplainedVariance      0.225534\n",
      "PolicyExecTime                               1.06463\n",
      "ProcessExecTime                              0.0716796\n",
      "TotalEnvSteps                            40276\n",
      "policy/Entropy                               1.16234\n",
      "policy/KL                                    0.00718544\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.173551\n",
      "policy/LossBefore                           -0.136116\n",
      "policy/Perplexity                            3.1974\n",
      "policy/dLoss                                 0.0374348\n",
      "---------------------------------------  --------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-06-17 23:39:39 | epoch #4 | Obtaining samples for iteration 4...\n",
      "2020-06-17 23:39:40 | epoch #4 | Logging diagnostics...\n",
      "2020-06-17 23:39:40 | epoch #4 | Optimizing policy...\n",
      "2020-06-17 23:39:40 | epoch #4 | Computing loss before\n",
      "2020-06-17 23:39:40 | epoch #4 | Computing KL before\n",
      "2020-06-17 23:39:40 | epoch #4 | Optimizing\n",
      "2020-06-17 23:39:40 | epoch #4 | Start CG optimization: #parameters: 1282, #inputs: 57, #subsample_inputs: 57\n",
      "2020-06-17 23:39:40 | epoch #4 | computing loss before\n",
      "2020-06-17 23:39:40 | epoch #4 | computing gradient\n",
      "2020-06-17 23:39:40 | epoch #4 | gradient computed\n",
      "2020-06-17 23:39:40 | epoch #4 | computing descent direction\n",
      "2020-06-17 23:39:41 | epoch #4 | descent direction computed\n",
      "2020-06-17 23:39:41 | epoch #4 | backtrack iters: 0\n",
      "2020-06-17 23:39:41 | epoch #4 | optimization finished\n",
      "2020-06-17 23:39:41 | epoch #4 | Computing KL after\n",
      "2020-06-17 23:39:41 | epoch #4 | Computing loss after\n",
      "2020-06-17 23:39:41 | epoch #4 | Fitting baseline...\n",
      "2020-06-17 23:39:41 | epoch #4 | Saving snapshot...\n",
      "2020-06-17 23:39:41 | epoch #4 | Saved\n",
      "2020-06-17 23:39:41 | epoch #4 | Time 12.18 s\n",
      "2020-06-17 23:39:41 | epoch #4 | EpochTime 1.71 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.258626\n",
      "Evaluation/AverageDiscountedReturn          81.5558\n",
      "Evaluation/AverageReturn                   176.421\n",
      "Evaluation/TerminationRate                   0.438596\n",
      "Evaluation/Iteration                         4\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                        37\n",
      "Evaluation/NumEpisodes                      57\n",
      "Evaluation/StdReturn                        36.1794\n",
      "Extras/EpisodeRewardMean                   150.6\n",
      "LinearFeatureBaseline/ExplainedVariance      0.680857\n",
      "PolicyExecTime                               1.06064\n",
      "ProcessExecTime                              0.0733292\n",
      "TotalEnvSteps                            50332\n",
      "policy/Entropy                               0.678223\n",
      "policy/KL                                    0.00403111\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.0399799\n",
      "policy/LossBefore                           -0.0267976\n",
      "policy/Perplexity                            1.97037\n",
      "policy/dLoss                                 0.0131823\n",
      "---------------------------------------  --------------\n",
      "2020-06-17 23:39:41 | epoch #5 | Obtaining samples for iteration 5...\n",
      "2020-06-17 23:39:42 | epoch #5 | Logging diagnostics...\n",
      "2020-06-17 23:39:42 | epoch #5 | Optimizing policy...\n",
      "2020-06-17 23:39:42 | epoch #5 | Computing loss before\n",
      "2020-06-17 23:39:42 | epoch #5 | Computing KL before\n",
      "2020-06-17 23:39:42 | epoch #5 | Optimizing\n",
      "2020-06-17 23:39:42 | epoch #5 | Start CG optimization: #parameters: 1282, #inputs: 53, #subsample_inputs: 53\n",
      "2020-06-17 23:39:42 | epoch #5 | computing loss before\n",
      "2020-06-17 23:39:42 | epoch #5 | computing gradient\n",
      "2020-06-17 23:39:42 | epoch #5 | gradient computed\n",
      "2020-06-17 23:39:42 | epoch #5 | computing descent direction\n",
      "2020-06-17 23:39:42 | epoch #5 | descent direction computed\n",
      "2020-06-17 23:39:42 | epoch #5 | backtrack iters: 0\n",
      "2020-06-17 23:39:42 | epoch #5 | optimization finished\n",
      "2020-06-17 23:39:42 | epoch #5 | Computing KL after\n",
      "2020-06-17 23:39:42 | epoch #5 | Computing loss after\n",
      "2020-06-17 23:39:42 | epoch #5 | Fitting baseline...\n",
      "2020-06-17 23:39:42 | epoch #5 | Saving snapshot...\n",
      "2020-06-17 23:39:42 | epoch #5 | Saved\n",
      "2020-06-17 23:39:42 | epoch #5 | Time 14.04 s\n",
      "2020-06-17 23:39:42 | epoch #5 | EpochTime 1.86 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.27885\n",
      "Evaluation/AverageDiscountedReturn          83.9113\n",
      "Evaluation/AverageReturn                   189.189\n",
      "Evaluation/TerminationRate                   0.283019\n",
      "Evaluation/Iteration                         5\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                        24\n",
      "Evaluation/NumEpisodes                      53\n",
      "Evaluation/StdReturn                        30.7105\n",
      "Extras/EpisodeRewardMean                   183.71\n",
      "LinearFeatureBaseline/ExplainedVariance      0.8973\n",
      "PolicyExecTime                               1.16195\n",
      "ProcessExecTime                              0.0753827\n",
      "TotalEnvSteps                            60359\n",
      "policy/Entropy                               0.634591\n",
      "policy/KL                                    0.00852306\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.0192794\n",
      "policy/LossBefore                           -0.012601\n",
      "policy/Perplexity                            1.88625\n",
      "policy/dLoss                                 0.00667836\n",
      "---------------------------------------  --------------\n",
      "2020-06-17 23:39:42 | epoch #6 | Obtaining samples for iteration 6...\n",
      "2020-06-17 23:39:44 | epoch #6 | Logging diagnostics...\n",
      "2020-06-17 23:39:44 | epoch #6 | Optimizing policy...\n",
      "2020-06-17 23:39:44 | epoch #6 | Computing loss before\n",
      "2020-06-17 23:39:44 | epoch #6 | Computing KL before\n",
      "2020-06-17 23:39:44 | epoch #6 | Optimizing\n",
      "2020-06-17 23:39:44 | epoch #6 | Start CG optimization: #parameters: 1282, #inputs: 52, #subsample_inputs: 52\n",
      "2020-06-17 23:39:44 | epoch #6 | computing loss before\n",
      "2020-06-17 23:39:44 | epoch #6 | computing gradient\n",
      "2020-06-17 23:39:44 | epoch #6 | gradient computed\n",
      "2020-06-17 23:39:44 | epoch #6 | computing descent direction\n",
      "2020-06-17 23:39:44 | epoch #6 | descent direction computed\n",
      "2020-06-17 23:39:44 | epoch #6 | backtrack iters: 1\n",
      "2020-06-17 23:39:44 | epoch #6 | optimization finished\n",
      "2020-06-17 23:39:44 | epoch #6 | Computing KL after\n",
      "2020-06-17 23:39:44 | epoch #6 | Computing loss after\n",
      "2020-06-17 23:39:44 | epoch #6 | Fitting baseline...\n",
      "2020-06-17 23:39:44 | epoch #6 | Saving snapshot...\n",
      "2020-06-17 23:39:44 | epoch #6 | Saved\n",
      "2020-06-17 23:39:44 | epoch #6 | Time 16.03 s\n",
      "2020-06-17 23:39:44 | epoch #6 | EpochTime 1.99 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.2926\n",
      "Evaluation/AverageDiscountedReturn          85.7675\n",
      "Evaluation/AverageReturn                   195.154\n",
      "Evaluation/TerminationRate                   0.134615\n",
      "Evaluation/Iteration                         6\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                       141\n",
      "Evaluation/NumEpisodes                      52\n",
      "Evaluation/StdReturn                        14.22\n",
      "Extras/EpisodeRewardMean                   191.88\n",
      "LinearFeatureBaseline/ExplainedVariance      0.96294\n",
      "PolicyExecTime                               1.24642\n",
      "ProcessExecTime                              0.0770645\n",
      "TotalEnvSteps                            70507\n",
      "policy/Entropy                               0.57757\n",
      "policy/KL                                    0.00642571\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.0143713\n",
      "policy/LossBefore                           -0.00525146\n",
      "policy/Perplexity                            1.7817\n",
      "policy/dLoss                                 0.00911981\n",
      "---------------------------------------  --------------\n",
      "2020-06-17 23:39:44 | epoch #7 | Obtaining samples for iteration 7...\n",
      "2020-06-17 23:39:46 | epoch #7 | Logging diagnostics...\n",
      "2020-06-17 23:39:46 | epoch #7 | Optimizing policy...\n",
      "2020-06-17 23:39:46 | epoch #7 | Computing loss before\n",
      "2020-06-17 23:39:46 | epoch #7 | Computing KL before\n",
      "2020-06-17 23:39:46 | epoch #7 | Optimizing\n",
      "2020-06-17 23:39:46 | epoch #7 | Start CG optimization: #parameters: 1282, #inputs: 52, #subsample_inputs: 52\n",
      "2020-06-17 23:39:46 | epoch #7 | computing loss before\n",
      "2020-06-17 23:39:46 | epoch #7 | computing gradient\n",
      "2020-06-17 23:39:46 | epoch #7 | gradient computed\n",
      "2020-06-17 23:39:46 | epoch #7 | computing descent direction\n",
      "2020-06-17 23:39:46 | epoch #7 | descent direction computed\n",
      "2020-06-17 23:39:46 | epoch #7 | backtrack iters: 1\n",
      "2020-06-17 23:39:46 | epoch #7 | optimization finished\n",
      "2020-06-17 23:39:46 | epoch #7 | Computing KL after\n",
      "2020-06-17 23:39:46 | epoch #7 | Computing loss after\n",
      "2020-06-17 23:39:46 | epoch #7 | Fitting baseline...\n",
      "2020-06-17 23:39:46 | epoch #7 | Saving snapshot...\n",
      "2020-06-17 23:39:46 | epoch #7 | Saved\n",
      "2020-06-17 23:39:46 | epoch #7 | Time 18.02 s\n",
      "2020-06-17 23:39:46 | epoch #7 | EpochTime 1.99 s\n",
      "---------------------------------------  --------------\n",
      "EnvExecTime                                  0.305299\n",
      "Evaluation/AverageDiscountedReturn          85.826\n",
      "Evaluation/AverageReturn                   196.5\n",
      "Evaluation/TerminationRate                   0.0384615\n",
      "Evaluation/Iteration                         7\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                       100\n",
      "Evaluation/NumEpisodes                      52\n",
      "Evaluation/StdReturn                        17.5888\n",
      "Extras/EpisodeRewardMean                   195.66\n",
      "LinearFeatureBaseline/ExplainedVariance      0.941885\n",
      "PolicyExecTime                               1.27374\n",
      "ProcessExecTime                              0.0784049\n",
      "TotalEnvSteps                            80725\n",
      "policy/Entropy                               0.592829\n",
      "policy/KL                                    0.00720406\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.0138152\n",
      "policy/LossBefore                           -0.00165256\n",
      "policy/Perplexity                            1.8091\n",
      "policy/dLoss                                 0.0121626\n",
      "---------------------------------------  --------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-06-17 23:39:46 | epoch #8 | Obtaining samples for iteration 8...\n",
      "2020-06-17 23:39:48 | epoch #8 | Logging diagnostics...\n",
      "2020-06-17 23:39:48 | epoch #8 | Optimizing policy...\n",
      "2020-06-17 23:39:48 | epoch #8 | Computing loss before\n",
      "2020-06-17 23:39:48 | epoch #8 | Computing KL before\n",
      "2020-06-17 23:39:48 | epoch #8 | Optimizing\n",
      "2020-06-17 23:39:48 | epoch #8 | Start CG optimization: #parameters: 1282, #inputs: 52, #subsample_inputs: 52\n",
      "2020-06-17 23:39:48 | epoch #8 | computing loss before\n",
      "2020-06-17 23:39:48 | epoch #8 | computing gradient\n",
      "2020-06-17 23:39:48 | epoch #8 | gradient computed\n",
      "2020-06-17 23:39:48 | epoch #8 | computing descent direction\n",
      "2020-06-17 23:39:48 | epoch #8 | descent direction computed\n",
      "2020-06-17 23:39:48 | epoch #8 | backtrack iters: 1\n",
      "2020-06-17 23:39:48 | epoch #8 | optimization finished\n",
      "2020-06-17 23:39:48 | epoch #8 | Computing KL after\n",
      "2020-06-17 23:39:48 | epoch #8 | Computing loss after\n",
      "2020-06-17 23:39:48 | epoch #8 | Fitting baseline...\n",
      "2020-06-17 23:39:48 | epoch #8 | Saving snapshot...\n",
      "2020-06-17 23:39:48 | epoch #8 | Saved\n",
      "2020-06-17 23:39:48 | epoch #8 | Time 19.98 s\n",
      "2020-06-17 23:39:48 | epoch #8 | EpochTime 1.96 s\n",
      "---------------------------------------  ---------------\n",
      "EnvExecTime                                  0.291724\n",
      "Evaluation/AverageDiscountedReturn          86.4935\n",
      "Evaluation/AverageReturn                   199.269\n",
      "Evaluation/TerminationRate                   0.0384615\n",
      "Evaluation/Iteration                         8\n",
      "Evaluation/MaxReturn                       200\n",
      "Evaluation/MinReturn                       179\n",
      "Evaluation/NumEpisodes                      52\n",
      "Evaluation/StdReturn                         3.67484\n",
      "Extras/EpisodeRewardMean                   197.8\n",
      "LinearFeatureBaseline/ExplainedVariance      0.994062\n",
      "PolicyExecTime                               1.24466\n",
      "ProcessExecTime                              0.0764556\n",
      "TotalEnvSteps                            91087\n",
      "policy/Entropy                               0.557682\n",
      "policy/KL                                    0.00952263\n",
      "policy/KLBefore                              0\n",
      "policy/LossAfter                            -0.00620555\n",
      "policy/LossBefore                            0.000908067\n",
      "policy/Perplexity                            1.74662\n",
      "policy/dLoss                                 0.00711362\n",
      "---------------------------------------  ---------------\n",
      "2020-06-17 23:39:48 | epoch #9 | Obtaining samples for iteration 9...\n",
      "2020-06-17 23:39:50 | epoch #9 | Logging diagnostics...\n",
      "2020-06-17 23:39:50 | epoch #9 | Optimizing policy...\n",
      "2020-06-17 23:39:50 | epoch #9 | Computing loss before\n",
      "2020-06-17 23:39:50 | epoch #9 | Computing KL before\n",
      "2020-06-17 23:39:50 | epoch #9 | Optimizing\n",
      "2020-06-17 23:39:50 | epoch #9 | Start CG optimization: #parameters: 1282, #inputs: 52, #subsample_inputs: 52\n",
      "2020-06-17 23:39:50 | epoch #9 | computing loss before\n",
      "2020-06-17 23:39:50 | epoch #9 | computing gradient\n",
      "2020-06-17 23:39:50 | epoch #9 | gradient computed\n",
      "2020-06-17 23:39:50 | epoch #9 | computing descent direction\n",
      "2020-06-17 23:39:50 | epoch #9 | descent direction computed\n",
      "2020-06-17 23:39:50 | epoch #9 | backtrack iters: 1\n",
      "2020-06-17 23:39:50 | epoch #9 | optimization finished\n",
      "2020-06-17 23:39:50 | epoch #9 | Computing KL after\n",
      "2020-06-17 23:39:50 | epoch #9 | Computing loss after\n",
      "2020-06-17 23:39:50 | epoch #9 | Fitting baseline...\n",
      "2020-06-17 23:39:50 | epoch #9 | Saving snapshot...\n",
      "2020-06-17 23:39:50 | epoch #9 | Saved\n",
      "2020-06-17 23:39:50 | epoch #9 | Time 21.98 s\n",
      "2020-06-17 23:39:50 | epoch #9 | EpochTime 2.00 s\n",
      "---------------------------------------  ----------------\n",
      "EnvExecTime                                   0.299378\n",
      "Evaluation/AverageDiscountedReturn           86.602\n",
      "Evaluation/AverageReturn                    200\n",
      "Evaluation/TerminationRate                    0\n",
      "Evaluation/Iteration                          9\n",
      "Evaluation/MaxReturn                        200\n",
      "Evaluation/MinReturn                        200\n",
      "Evaluation/NumEpisodes                       52\n",
      "Evaluation/StdReturn                          0\n",
      "Extras/EpisodeRewardMean                    199.62\n",
      "LinearFeatureBaseline/ExplainedVariance       0.998629\n",
      "PolicyExecTime                                1.26204\n",
      "ProcessExecTime                               0.0775559\n",
      "TotalEnvSteps                            101487\n",
      "policy/Entropy                                0.55067\n",
      "policy/KL                                     0.00642275\n",
      "policy/KLBefore                               0\n",
      "policy/LossAfter                             -0.012962\n",
      "policy/LossBefore                            -1.46719e-09\n",
      "policy/Perplexity                             1.73442\n",
      "policy/dLoss                                  0.012962\n",
      "---------------------------------------  ----------------\n",
      "done at step 875\n"
     ]
    }
   ],
   "source": [
    "trpo_gym_tf_cartpole(ctxt=ctxt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "colab_type": "code",
    "id": "HKAAXIuaZQo-",
    "outputId": "e72c4b26-7385-4315-ed87-473098e19e09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: glob2 in /Users/irisliu/.pyenv/versions/3.7.2/lib/python3.7/site-packages (0.7)\n",
      "\u001b[33mYou are using pip version 18.1, however version 20.2b1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <video width=\"360\" height=\"auto\" alt=\"test\" controls><source 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type=\"video/mp4\" /></video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the video in the jupyter notebook\n",
    "# Click the play button below to watch the video\n",
    "!pip install glob2\n",
    "\n",
    "import io\n",
    "import base64\n",
    "import glob2\n",
    "from IPython.display import HTML\n",
    "\n",
    "latest_video_name = glob2.iglob('./gym-results/*.mp4')\n",
    "video = io.open(list(latest_video_name)[-1], 'r+b').read()\n",
    "encoded = base64.b64encode(video)\n",
    "\n",
    "\n",
    "vid_ascii = encoded.decode('ascii')\n",
    "\n",
    "# play the last video\n",
    "HTML(data='''\n",
    "        <video width=\"360\" height=\"auto\" alt=\"test\" controls><source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" /></video>'''\n",
    "    .format(vid_ascii))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "trpo_gym_tf_cartpole.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
